Tunable filtering of voice-related components from motion sensor

ABSTRACT

To protect a user&#39;s privacy by reducing a malicious developer&#39;s ability to eavesdrop on unwitting HMD users by converting signals from a motion sensor in the HMD to speech or speaker recognition, a microphone can record ambient sound and voice which is subtracted from the motion sensor data before the sensor data is made available to the game developer. In another technique, a band pass filter subtracts frequency in the sensor signals within the voice range. Still a third technique blends statistical noise into the motion sensor signal before passing to game developers to obfuscate the user&#39;s speech. The amount by which voice components in the motion signal are eliminated or obfuscated can be tuned by a person or app.

FIELD

The present application relates generally to tunable filtering of voice-related components from motion sensor signals.

BACKGROUND

Motion sensors may be mounted on a head-mounted display (HMD) and provided to an application such as a computer game application for use by the application in presenting virtual reality (VR) or augmented reality (AR) video on the display consistent with the pose of the head of the wearer of the HMD.

As recognized herein, by mounting motion sensors on an HMD or for that matter other device (game controller, cell phone), speech and speaker identity can be inferred even without the use of a microphone. This is because motion sensors are extremely sensitive, and those mounted on the head can pick up subtle vibrations from a user wearing an HMD while speaking. Unfortunately, this can lead a user to potentially have his privacy violated by leaking speech when he believes the microphone on the HMD is off (or not even present).

SUMMARY

Present principles help protect a user's privacy by reducing a malicious developer's ability to eavesdrop on unwitting HMD users according to a specified level of filtering.

Accordingly, an assembly includes at least one device with at least one motion sensor configured to generate signals. The assembly also includes at least one processor configured with instructions to receive a tuning signal indicating an amount of removal and/or obfuscation. The instructions are executable to remove and/or obfuscate voice-caused components from the signals generated by the motion sensor according to the tuning signal to render a first signal, and transmit the first signal to an application using motion sensor signals.

In one example, the tuning signal is received from input to a user interface (UI). In another example, the tuning signal is received from a computer simulation console. In another example, the tuning signal is received from a computer simulation engine.

In some embodiments, the instructions can be executable to remove voice-caused components from the signals generated by the motion sensor in a first frequency range responsive to a first tuning signal, and remove voice-caused components from the signals generated by the motion sensor in a second frequency range responsive to a second tuning signal.

In example implementations, the instructions may be executable to obfuscate voice-caused components from the signals generated by the motion sensor in a first frequency range responsive to a first tuning signal, and obfuscate voice-caused components from the signals generated by the motion sensor in a second frequency range responsive to a second tuning signal.

In another aspect, a method includes receiving signals from at least one motion sensor on at least one head-mounted display (HMD). The method also includes receiving a tuning signal representing a tuning amount, and obfuscating and/or removing voice-generated components in the signals from the at least one motion sensor according to the tuning amount.

In another aspect, a head-mounted display (HMD) for generating demanded images of at least one computer simulation includes at least one processor and at least one motion sensor configured to generate motion signals. The processor is configured with instructions for removing and/or obfuscating portions of the motion signals decodable for speech recognition according to a tuning signal.

The details of the present application, both as to its structure and operation, can be best understood in reference to the accompanying drawings, in which like reference numerals refer to like parts, and in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system in accordance with present principles;

FIG. 2 illustrates a first embodiment of a HMD;

FIG. 3 illustrates a second embodiment of a non-limiting example HMD;

FIG. 4 illustrates a block diagram of example components of an HMD;

FIG. 5 illustrates an example signal flow diagram;

FIG. 6 illustrates in example flow chart format example logic for a first technique for removing voice signals in motion sensor signals;

FIG. 7 illustrates an example signal flow diagram for a second technique for removing voice signals in motion sensor signals;

FIG. 8 illustrates an example signal flow diagram for a second technique for obfuscating voice signals in motion sensor signals;

FIG. 9 illustrates example logic in example flow chart format for tuning the amount of voice component filtering from motion signals;

FIG. 10 illustrates an example user interface for establishing the amount of tuning;

FIG. 11 illustrates example logic in example flow chart format for an alternate technique for establishing the amount of tuning; and

FIG. 12 illustrates more detailed logic in example flow chart format for tuning the amount of voice component filtering from motion signals.

DETAILED DESCRIPTION

This disclosure relates generally to computer ecosystems including aspects of consumer electronics (CE) device networks such as but not limited to computer game networks. A system herein may include server and client components which may be connected over a network such that data may be exchanged between the client and server components. The client components may include one or more computing devices including game consoles such as Sony PlayStation® or a game console made by Microsoft or Nintendo or other manufacturer, extended reality (XR) headsets such as virtual reality (VR) headsets, augmented reality (AR) headsets, portable televisions (e.g., smart TVs, Internet-enabled TVs), portable computers such as laptops and tablet computers, and other mobile devices including smart phones and additional examples discussed below. These client devices may operate with a variety of operating environments. For example, some of the client computers may employ, as examples, Linux operating systems, operating systems from Microsoft, or a Unix operating system, or operating systems produced by Apple, Inc., or Google, or a Berkeley Software Distribution or Berkeley Standard Distribution (BSD) OS including descendants of BSD. These operating environments may be used to execute one or more browsing programs, such as a browser made by Microsoft or Google or Mozilla or other browser program that can access websites hosted by the Internet servers discussed below. Also, an operating environment according to present principles may be used to execute one or more computer game programs.

Servers and/or gateways may be used that may include one or more processors executing instructions that configure the servers to receive and transmit data over a network such as the Internet. Or a client and server can be connected over a local intranet or a virtual private network. A server or controller may be instantiated by a game console such as a Sony PlayStation®, a personal computer, etc.

Information may be exchanged over a network between the clients and servers. To this end and for security, servers and/or clients can include firewalls, load balancers, temporary storages, and proxies, and other network infrastructure for reliability and security. One or more servers may form an apparatus that implement methods of providing a secure community such as an online social website or gamer network to network members.

A processor may be a single- or multi-chip processor that can execute logic by means of various lines such as address lines, data lines, and control lines and registers and shift registers.

Components included in one embodiment can be used in other embodiments in any appropriate combination. For example, any of the various components described herein and/or depicted in the Figures may be combined, interchanged, or excluded from other embodiments.

“A system having at least one of A, B, and C” (likewise “a system having at least one of A, B, or C” and “a system having at least one of A, B, C”) includes systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together.

Referring now to FIG. 1 , an example system 10 is shown, which may include one or more of the example devices mentioned above and described further below in accordance with present principles. The first of the example devices included in the system 10 is a consumer electronics (CE) device such as an audio video device (AVD) 12 such as but not limited to a theater display system which may be projector-based, or an Internet-enabled TV with a TV tuner (equivalently, set top box controlling a TV). The AVD 12 alternatively may also be a computerized Internet enabled (“smart”) telephone, a tablet computer, a notebook computer, a head-mounted device (HMD) and/or headset such as smart glasses or a VR headset, another wearable computerized device, a computerized Internet-enabled music player, computerized Internet-enabled headphones, a computerized Internet-enabled implantable device such as an implantable skin device, etc. Regardless, it is to be understood that the AVD 12 is configured to undertake present principles (e.g., communicate with other CE devices to undertake present principles, execute the logic described herein, and perform any other functions and/or operations described herein).

Accordingly, to undertake such principles the AVD 12 can be established by some, or all of the components shown. For example, the AVD 12 can include one or more touch-enabled displays 14 that may be implemented by a high definition or ultra-high definition “4K” or higher flat screen. The touch-enabled display(s) 14 may include, for example, a capacitive or resistive touch sensing layer with a grid of electrodes for touch sensing consistent with present principles.

The AVD 12 may also include one or more speakers 16 for outputting audio in accordance with present principles, and at least one additional input device 18 such as an audio receiver/microphone for entering audible commands to the AVD 12 to control the AVD 12. The example AVD 12 may also include one or more network interfaces 20 for communication over at least one network 22 such as the Internet, an WAN, an LAN, etc. under control of one or more processors 24. Thus, the interface 20 may be, without limitation, a Wi-Fi transceiver, which is an example of a wireless computer network interface, such as but not limited to a mesh network transceiver. It is to be understood that the processor 24 controls the AVD 12 to undertake present principles, including the other elements of the AVD 12 described herein such as controlling the display 14 to present images thereon and receiving input therefrom. Furthermore, note the network interface 20 may be a wired or wireless modem or router, or other appropriate interface such as a wireless telephony transceiver, or Wi-Fi transceiver as mentioned above, etc.

In addition to the foregoing, the AVD 12 may also include one or more input and/or output ports 26 such as a high-definition multimedia interface (HDMI) port or a universal serial bus (USB) port to physically connect to another CE device and/or a headphone port to connect headphones to the AVD 12 for presentation of audio from the AVD 12 to a user through the headphones. For example, the input port 26 may be connected via wire or wirelessly to a cable or satellite source 26 a of audio video content. Thus, the source 26 a may be a separate or integrated set top box, or a satellite receiver. Or the source 26 a may be a game console or disk player containing content. The source 26 a when implemented as a game console may include some or all of the components described below in relation to the CE device 48.

The AVD 12 may further include one or more computer memories/computer-readable storage media 28 such as disk-based or solid-state storage that are not transitory signals, in some cases embodied in the chassis of the AVD as standalone devices or as a personal video recording device (PVR) or video disk player either internal or external to the chassis of the AVD for playing back AV programs or as removable memory media or the below-described server. Also, in some embodiments, the AVD 12 can include a position or location receiver such as but not limited to a cellphone receiver, GPS receiver and/or altimeter 30 that is configured to receive geographic position information from a satellite or cellphone base station and provide the information to the processor 24 and/or determine an altitude at which the AVD 12 is disposed in conjunction with the processor 24.

Continuing the description of the AVD 12, in some embodiments the AVD 12 may include one or more cameras 32 that may be a thermal imaging camera, a digital camera such as a webcam, an IR sensor, an event-based sensor, and/or a camera integrated into the AVD 12 and controllable by the processor 24 to gather pictures/images and/or video in accordance with present principles. Also included on the AVD 12 may be a Bluetooth® transceiver 34 and other Near Field Communication (NFC) element 36 for communication with other devices using Bluetooth and/or NFC technology, respectively. An example NFC element can be a radio frequency identification (RFID) element.

Further still, the AVD 12 may include one or more auxiliary sensors 38 that provide input to the processor 24. For example, one or more of the auxiliary sensors 38 may include one or more pressure sensors forming a layer of the touch-enabled display 14 itself and may be, without limitation, piezoelectric pressure sensors, capacitive pressure sensors, piezoresistive strain gauges, optical pressure sensors, electromagnetic pressure sensors, etc. Other sensor examples include a pressure sensor, a motion sensor such as an accelerometer, gyroscope, cyclometer, or a magnetic sensor, an infrared (IR) sensor, an optical sensor, a speed and/or cadence sensor, an event-based sensor, a gesture sensor (e.g., for sensing gesture command). The sensor 38 thus may be implemented by one or more motion sensors, such as individual accelerometers, gyroscopes, and magnetometers and/or an inertial measurement unit (IMU) that typically includes a combination of accelerometers, gyroscopes, and magnetometers to determine the location and orientation of the AVD 12 in three dimension or by an event-based sensors such as event detection sensors (EDS). An EDS consistent with the present disclosure provides an output that indicates a change in light intensity sensed by at least one pixel of a light sensing array. For example, if the light sensed by a pixel is decreasing, the output of the EDS may be −1; if it is increasing, the output of the EDS may be a +1. No change in light intensity below a certain threshold may be indicated by an output binary signal of 0.

The AVD 12 may also include an over-the-air TV broadcast port 40 for receiving OTA TV broadcasts providing input to the processor 24. In addition to the foregoing, it is noted that the AVD 12 may also include an infrared (IR) transmitter and/or IR receiver and/or IR transceiver 42 such as an IR data association (IRDA) device. A battery (not shown) may be provided for powering the AVD 12, as may be a kinetic energy harvester that may turn kinetic energy into power to charge the battery and/or power the AVD 12. A graphics processing unit (GPU) 44 and field programmable gated array 46 also may be included. One or more haptics/vibration generators 47 may be provided for generating tactile signals that can be sensed by a person holding or in contact with the device. The haptics generators 47 may thus vibrate all or part of the AVD 12 using an electric motor connected to an off-center and/or off-balanced weight via the motor's rotatable shaft so that the shaft may rotate under control of the motor (which in turn may be controlled by a processor such as the processor 24) to create vibration of various frequencies and/or amplitudes as well as force simulations in various directions.

A light source such as a projector such as an infrared (IR) projector also may be included.

In addition to the AVD 12, the system 10 may include one or more other CE device types. In one example, a first CE device 48 may be a computer game console that can be used to send computer game audio and video to the AVD 12 via commands sent directly to the AVD 12 and/or through the below-described server while a second CE device 50 may include similar components as the first CE device 48. In the example shown, the second CE device 50 may be configured as a computer game controller manipulated by a player or a head-mounted display (HMD) worn by a player. The HMD may include a heads-up transparent or non-transparent display for respectively presenting AR/MR content or VR content (more generally, extended reality (XR) content). The HMD may be configured as a glasses-type display or as a bulkier VR-type display vended by computer game equipment manufacturers.

In the example shown, only two CE devices are shown, it being understood that fewer or greater devices may be used. A device herein may implement some or all of the components shown for the AVD 12. Any of the components shown in the following figures may incorporate some or all of the components shown in the case of the AVD 12.

Now in reference to the afore-mentioned at least one server 52, it includes at least one server processor 54, at least one tangible computer readable storage medium 56 such as disk-based or solid-state storage, and at least one network interface 58 that, under control of the server processor 54, allows for communication with the other illustrated devices over the network 22, and indeed may facilitate communication between servers and client devices in accordance with present principles. Note that the network interface 58 may be, e.g., a wired or wireless modem or router, Wi-Fi transceiver, or other appropriate interface such as, e.g., a wireless telephony transceiver.

Accordingly, in some embodiments the server 52 may be an Internet server or an entire server “farm” and may include and perform “cloud” functions such that the devices of the system 10 may access a “cloud” environment via the server 52 in example embodiments for, e.g., network gaming applications. Or the server 52 may be implemented by one or more game consoles or other computers in the same room as the other devices shown or nearby.

The components shown in the following figures may include some or all components shown in herein. Any user interfaces (UI) described herein may be consolidated and/or expanded, and UI elements may be mixed and matched between UIs.

Present principles may employ various machine learning models, including deep learning models. Machine learning models consistent with present principles may use various algorithms trained in ways that include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, feature learning, self-learning, and other forms of learning. Examples of such algorithms, which can be implemented by computer circuitry, include one or more neural networks, such as a convolutional neural network (CNN), a recurrent neural network (RNN), and a type of RNN known as a long short-term memory (LSTM) network. Support vector machines (SVM) and Bayesian networks also may be considered to be examples of machine learning models. In addition to the types of networks set forth above, models herein may be implemented by classifiers.

As understood herein, performing machine learning may therefore involve accessing and then training a model on training data to enable the model to process further data to make inferences. An artificial neural network/artificial intelligence model trained through machine learning may thus include an input layer, an output layer, and multiple hidden layers in between that that are configured and weighted to make inferences about an appropriate output.

FIGS. 2 and 3 illustrate two non-limiting example embodiments of a HMD. The HMD 200 shown in FIG. 2 is a VR HMD with an oval-shaped display 202 mounted on a headband 204 that encircles the head of a wearer.

In contrast, the HMD 300 shown in FIG. 3 is configured as eyeglasses with left and right lens-shaped displays 302 mounted in an eyeglass-style frame 304 having a nose bridge 306 between the lenses 302 and left and right templates 308 extending back to lie against a wearer's head.

Note that present principles are not necessarily limited to HMDs. Other components with motions sensors, such as, for example, computer game controllers and cell phones, may incorporate present principles.

FIG. 4 illustrates example non-limiting components of a device with a motion sensor such as an HMD or computer game controller or cell phone consistent with present principles. One or more processors 400 may access instructions on one or more computer storages or memories 402 to configure the processor to execute techniques described herein. The processor 400 may control one of more displays 404 and receive signals from one or more motion sensors 406 such as any one or more of the motion sensors divulged herein. The processor 400 also may receive input from one or more microphones 408 and may output motion signals generated by the motion sensor 406 to one or more applications 410 such as one or more developer applications such as computer game applications.

Additionally, the processor 400 may access one or more of a bandpass filter 412, an ACN 414, a noise cancelation engine or algorithm 416, and a noise generator 418 such as a pseudo-random (PN) noise and/or white noise generator, for purposes to be shortly disclosed.

FIG. 5 illustrates an overall signal processing flow for obfuscating or removing voice-generated components in the signals 500 from the motion sensor 406 shown in FIG. 4 . A speech obfuscator module 502 generates noise or microphone signals to add to the motion sensor signals 500 at state 504 or filters motion sensor signals at state 504. The resulting motion sensor signals, with voice-generated components either removed or obfuscated, are then sent to an application 506 requiring motion signals representing motion of the head of a wearer of the HMD, such as the developer app 410 shown in FIG. 4 .

Note that the logic of FIG. 5 may be enabled all of the time during use, or only some of the time during use, such as when the microphone of a HMD is muted or unavailable to the application. When the microphone is not muted or unavailable to an application, there may be little reason to obfuscate or remove speech components in motion signals since the application already has speech from the microphone. It is further to be noted that while the microphone 408 in FIG. 4 may have been muted by the user or may have been rendered unavailable to an application requiring motion signals, signals from the microphone may still be used internally by the HMD processor to remove voice components of motion signals.

It is noted that human voice frequencies may typically fall in the range of one to four kilohertz (1-4 KHz). For men, the fundamental voice frequency may fall in the range of eighty-five to one hundred fifty-five Hertz (85-155 Hz), and the fundamental voice frequency for women may fall in the range of one hundred sixty-five to two hundred fifty-five Hertz (165-255 Hz). However, the range of one to four kilohertz (1-4 KHz) may be of higher importance for speech intelligibility, and in one example this is the range into which noise is introduced or energy removed from the signal from the motion sensor, it being understood that the fundamental frequency ranges alternatively or additionally may be used.

Referring now to FIG. 6 , in a first technique motions signals from the motion sensor(s) such as the motion sensor 406 shown in FIG. 4 are received by the processor such as the processor 400 in FIG. 4 at block 600. Proceeding to block 602, voice-generated signals may be received from microphone(s) such as the microphone 408 shown in FIG. 4 , with the two sets of signals being temporally aligned at block 604 by use of, e.g., time stamps generated by the component producing the signals or by the processor upon time of receipt. The signals from the microphone are subtracted from the signals from the motion sensor at block 606 prior to sending the motion signals to an app such as the app 410 shown in FIG. 4 .

Thus, the presence of a microphone can help in removal of the speech signal from the input. The microphone can be used to record ambient sound and voice, and noise cancellation algorithms can be employed to subtract that signal from the subtle vibrations in the motion sensor data before the sensor data is made available to the game developer. Thus, the technique of FIG. 6 subtracts signals picked up by the microphone from signals generated by the motion sensor by the same physical act (speaking) that generated the microphone signals. ANC (active noise cancellation) techniques can be adapted to cancel noise from a motion sensor's data.

FIG. 7 illustrates a second technique in which signals from the motion sensor(s) 406 shown in FIG. 4 are sent through one or more filters 412 such as the bandpass filter. After filtering, the motion signals are sent to an app such as the app 410 shown in FIG. 4 .

Accordingly, a band pass filter can be used to naively subtract signals within the voice range, for example 1-4 kHz, from the motion sensor data before passing the motion signals to a game developer app 410. A smaller range may be filtered that still results in an acceptable degradation of intelligibility of speech components in the motion signals.

This is particularly effective if the user requests a heightened security mode. Although the filter might potentially remove some useful information from the motion data, in general a player's head is unable to move at the frequency range of voice signals, so the performance degradation should be minimal, with the exception of the latency introduced by such a filter.

FIG. 8 illustrates yet a third embodiment in which signals from a motion sensor such as the motion sensor 406 shown in FIG. 4 are obfuscated at addition state 800 by adding statistical noise from, e.g., the PN noise generator 418 shown in FIG. 4 and/or from a white noise generator or other sound signal generator, and then, after obfuscation, are sent to an app such as the developer app 410 shown in FIG. 4 . Thus, statistical noise can be blended into the motion sensor signal before passing the motion signals to game developer apps. This is the equivalent of a white noise generator or running the shower or radio in the background while trying to have a private conversation. This can help to obfuscate the player's speech since it is combined with additional speech-like noise. The noise preferably is generated in the entire spectrum of the human voice frequency range, e.g., 1-4 kHz, although a smaller range may be used that still results in an acceptable degradation of intelligibility of speech components in the motion signals.

Noises from different environment than the environment of the motion sensor may be blended into the motion sensor signal so a malicious developer cannot anticipate what noise might be blended in and remove it.

Machine learning (ML) may be used in the techniques described herein. For example, a ML model may be used in the technique of FIG. 6 to learn voice-caused components in the motion sensor signals. Such a model may be trained on data of motion sensor signals with ground truth indications of which components were caused by speech.

ML may be used in the techniques of FIGS. 7 and 8 to learn types of noises and decide whether a particular component in a type of noise should be removed or obfuscated by added-in noise. A ML model may be trained on data indicating noise types with ground truth labels, frequencies, etc.

The techniques of FIGS. 6-8 may be used individually to the exclusion of other techniques, or two or more of the techniques of FIGS. 6-8 may be implemented simultaneously by a HMD. All three techniques may be implemented by an HMD. A user may be given the option to enable and disable techniques herein by selecting an appropriate selector on a user interface audibly or visually or tactilely presented on any device described herein to obfuscate or remove voice-generated components of motion signals.

FIGS. 9 and 10 illustrate a tenability feature of principles above, in which the amount of voice component obfuscation/removal in motion sensor signals can be variably established. Commencing at block 900 in FIG. 9 , a tuning signal is received, indicating an amount by which voice components in motion signals should be removed and/or obfuscated, for example using any one or more of the techniques described above. Moving to block 902, the voice components in a motion signal from a motion sensor are removed and/or obfuscated to the degree indicated by the tuning signal received at block 900.

FIG. 10 illustrates a first example technique for establishing the tuning signal at block 900 in FIG. 9 . A display 1000 such as any display described herein can be present a user interface (UI) 1002 prompting a user to input a desired filtering level to indicate an amount of removal/obfuscation of voice components in motion signals. In the non-limiting embodiment shown, a data entry element such as a slide bar 1004 can be presented in which the user, can be means of a point-and-click device for instance, move a slider 1006 along the slide bar between minimum and maximum filtering ends of the slide bar to establish the amount of removal/obfuscation of voice components in motion signals to be implemented at block 902 in FIG. 9 , with a first slider position near the left end of the slide bar 104 establishing a low amount of filtering and a second first slider position near the right end of the slide bar 104 establishing a higher amount of filtering.

FIG. 11 illustrates a second example technique for establishing the tuning signal at block 900 in FIG. 9 . A game console or game engine 1100 may send a demanded tuning signal 1102 to a tuning processor 1104 executing the logic of FIG. 9 to establish the amount of removal/obfuscation of voice components in motion signals to be implemented at block 902 in FIG. 9 .

FIG. 12 illustrates further example logic consistent with principles herein in example non-limiting flow chart format. If a low or lowest tuning signal is determined to be received at decision diamond 1200, a lightest filtering technique is selected at block 1202 and used to remove and/or obfuscate voice components in motion signals at block 1204.

A non-limiting example of a lightest filtering technique is use of only one of the three techniques in respective FIGS. 6, 7, and 8 . Another non-limiting example of a lightest filtering technique is use of the voice component removal technique of FIG. 6 based on microphone signals, removing only voice components in a limited band of the motion signal spectrum, e.g., between two and two and a half kilohertz (2-2.5 KHz). Another non-limiting example of a lightest filtering technique is use of the voice component removal technique of FIG. 7 based on using a bandpass filter of relatively narrow range, e.g., between two and a half and three kilohertz (2.5-3 KHz). Another non-limiting example of a lightest filtering technique is use of the voice component obfuscation technique of FIG. 8 using a noise generator by injecting only a low level of noise into the motion signal, and/or by injecting noise only into a narrow band of the motion signal, e.g., between one and two kilohertz (1-2 KHz).

FIG. 12 indicates that a highest level of filtering may be determined to have been received at lock 1206, in which case a high or “heavy” filtering technique is selected at block 1208 and used to remove and/or obfuscate voice components in motion signals at block 1204.

A non-limiting example of a highest or heaviest filtering technique is use of more than one of the three techniques in respective FIGS. 6, 7, and 8 , such as the use of all three techniques. Another non-limiting example of a highest or heaviest filtering technique is use of the voice component removal technique of FIG. 6 based on microphone signals, removing signal components in a relatively wide band of the motion signal spectrum, e.g., between one-half and four kilohertz (0.5-4.0 KHz). Another non-limiting example of a highest or heaviest filtering technique is use of the voice component removal technique of FIG. 7 based on using a bandpass filter of relatively wide range, e.g., between one-half and five kilohertz (0.5-5 KHz). Another non-limiting example of a highest or heaviest filtering technique is use of the voice component obfuscation technique of FIG. 8 using a noise generator by injecting a relatively high level of noise into the motion signal, and/or by injecting noise into a wide band of the motion signal, e.g., between one quarter and four kilohertz (0.25-4 KHz).

Block 1210 indicates that medium effect filtering techniques between highest and lowest may be selected to remove/obfuscate voice components in motion signals at block 1204. It will readily be appreciated that such medium effect filtering techniques falls between the lowest and highest filtering techniques.

While the particular embodiments are herein shown and described in detail, it is to be understood that the subject matter which is encompassed by the present invention is limited only by the claims. 

What is claimed is:
 1. An assembly, comprising: at least one device comprising at least one motion sensor configured to generate signals; and at least one processor configured with instructions to: receive a tuning signal indicating an amount of removal and/or obfuscation; remove and/or obfuscate voice-caused components from the signals generated by the motion sensor according to the tuning signal to render a first signal; and transmit the first signal to an application using motion sensor signals.
 2. The assembly of claim 1, wherein the instructions are executable to remove voice-caused components from the signals generated by the motion sensor.
 3. The assembly of claim 1, wherein the instructions are executable to obfuscate voice-caused components from the signals generated by the motion sensor.
 4. The assembly of claim 1, wherein the tuning signal is received from input to a user interface (UI).
 5. The assembly of claim 1, wherein the tuning signal is received from a computer simulation console.
 6. The assembly of claim 1, wherein the tuning signal is received from a computer simulation engine.
 7. The assembly of claim 2, wherein the instructions are executable to: remove voice-caused components from the signals generated by the motion sensor in a first frequency range responsive to a first tuning signal; and remove voice-caused components from the signals generated by the motion sensor in a second frequency range responsive to a second tuning signal.
 8. The assembly of claim 3, wherein the instructions are executable to: obfuscate voice-caused components from the signals generated by the motion sensor in a first frequency range responsive to a first tuning signal; and obfuscate voice-caused components from the signals generated by the motion sensor in a second frequency range responsive to a second tuning signal.
 9. A method comprising: receiving signals from at least one motion sensor on at least one head-mounted display (HMD); receiving a tuning signal representing a tuning amount; and obfuscating and/or removing voice-generated components in the signals from the at least one motion sensor according to the tuning amount.
 10. The method of claim 9, comprising removing voice-generated components in the signals.
 11. The method of claim 9, comprising obfuscating voice-generated components in the signals.
 12. The method of claim 9, comprising receiving the tuning signal from input to a user interface (UI).
 13. The method of claim 9, comprising receiving the tuning signal from a computer simulation console.
 14. The method of claim 9, comprising receiving the tuning signal from a computer simulation engine.
 15. A head-mounted display (HMD) for generating demanded images of at least one computer simulation, comprising: at least one processor; at least one motion sensor configured to generate motion signals; and the processor being configured with instructions for removing and/or obfuscating portions of the motion signals decodable for speech recognition according to a tuning signal.
 16. The HMD of claim 15, wherein the tuning signal is received from input to a user interface (UI).
 17. The HMD of claim 15, wherein the tuning signal is received from a computer simulation console.
 18. The HMD of claim 15, wherein the tuning signal is received from a computer simulation engine.
 19. The HMD of claim 15, wherein the instructions are executable to: remove the portions of the motion signals decodable for speech recognition in a first frequency range responsive to a first tuning signal; and remove the portions of the motion signals decodable for speech recognition in a second frequency range responsive to a second tuning signal.
 20. The HMD of claim 15, wherein the instructions are executable to: obfuscate the portions of the motion signals decodable for speech recognition in a first frequency range responsive to a first tuning signal; and obfuscate the portions of the motion signals decodable for speech recognition in a second frequency range responsive to a second tuning signal. 